Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers
This addresses the impracticality of manual collision avoidance for operators managing thousands of satellites in constellations, offering an incremental improvement through automation.
The paper tackles the problem of increasing collision threats from growing space objects by proposing Space Navigator, a modular autonomous collision avoidance system that uses a novel optimization approach combining domain knowledge with Reinforcement Learning, resulting in an automated solution to replace manual handling.
The number of space objects will grow several times in a few years due to the planned launches of constellations of thousands microsatellites. It leads to a significant increase in the threat of satellite collisions. Spacecraft must undertake collision avoidance maneuvers to mitigate the risk. According to publicly available information, conjunction events are now manually handled by operators on the Earth. The manual maneuver planning requires qualified personnel and will be impractical for constellations of thousands satellites. In this paper we propose a new modular autonomous collision avoidance system called "Space Navigator". It is based on a novel maneuver optimization approach that combines domain knowledge with Reinforcement Learning methods.